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Advanced Quantum Technologies

Enhanced Quantum Synchronization via Quantum Machine Learning (Adv. Quantum Technol. 7‐8/2019)

F. A. Cárdenas-López, Mikel Sanz, J. C. Retamal, E. Solano

Published Aug 1, 2019• 0 citations
Publisher

Abstract

The cover figure represents a system comprising two atoms, which are called Agent and Environment, coupled to a central atom, called Register, in a dissipative reservoir. Additionally, a laser shines the Agent atom to counterbalance the losses. The machine learning protocol consists of measuring the Register and, according to the measurement outcome, act on the adjacent systems via classical communication. This protocol speeds up the quantum synchronization between Agent and Environment. For further information see article number 1800076 by Mikel Sanz and co-workers.

DOI
10.1002/qute.201970043

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The cover figure represents a system comprising two atoms, which are called Agent and Environment, coupled to a central atom, called Register, in a dissipative reservoir. Additionally, a laser shines the Agent atom to counterbalance the losses. The machine learning protocol consists of measuring the Register and, according to the measurement outcome, act on the adjacent systems via classical communication. This protocol speeds up the quantum synchronization between Agent and Environment. For further information see article number 1800076 by Mikel Sanz and co-workers.
Abstract The quantum synchronization between a pair of two‐level systems inside two coupled cavities is studied. By using a digital–analog decomposition of the master equation that rules the system dynamics, it is shown that this approach leads to quantum synchronization between both two‐level systems. Moreover, in this digital–analog block decomposition, the fundamental elements of a quantum machine learning protocol can be identified, in which the agent and the environment (learning units) interact through a mediating system, namely, the register. If the algorithm can be additionally equipped with a classical feedback mechanism, which consists of projective measurements in the register, reinitialization of the register state, and local conditional operations on the agent and environment subspace, a powerful and flexible quantum machine learning protocol emerges. Indeed, numerical simulations show that this protocol enhances the synchronization process, even when every subsystem experiences different loss/decoherence mechanisms, and gives the flexibility to choose the synchronization state. Finally, an implementation is proposed, based on current technologies in superconducting circuits.
As the field of artificial intelligence is pushed forward, the question arises of how fast autonomous machines can learn. Within artificial intelligence, an important paradigm is reinforcement learning, where agents - learning entities capable of decision making - interact with the world they are placed in, called an environment. Thanks to these interactions, agents receive feedback from the environment and thus progressively adjust their behaviour to accomplish a given goal. An important question in reinforcement learning is how fast agents can learn to fulfill their tasks. To answer this question we consider a novel reinforcement learning framework where quantum mechanics is used. In particular, we quantize the agent and the environment and grant them the possibility to also interact quantum-mechanically, that is, by using a quantum channel for their communication. We demonstrate that this feature enables a speed-up in the agent's learning process, and we further show that combining this scenario with classical communication enables the evaluation of such an improvement. This learning protocol is implemented on an integrated re-programmable photonic platform interfaced with photons at telecommunication wavelengths. Thanks to the full tunability of the device, this platform proves the best candidate for the implementation of learning protocols, where a continuous update of the learning process is required.
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We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.
We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.